论文标题
在基于主要曲线的分类器和基于相似性的选择性抽样上
On Principal Curve-Based Classifiers and Similarity-Based Selective Sampling in Time-Series
论文作者
论文摘要
考虑到时间稀释的概念，复发神经体系结构存在一些主要问题。输入数据点之间的任何时间跨度的任何变化都会导致复发性神经网络体系结构中的性能衰减。基于主曲线的分类器具有处理时间跨度任何类型的变化的能力。换句话说，基于主曲线的分类器可以保留时间的相对性，而神经网络体系结构违反了时间的属性。另一方面，考虑到在线监视设备中的标签成本和问题，应该有一种算法找到数据点，这些数据点知道它们的标签会导致分类器的更好性能。由于提出的算法的随机性，当前的选择性采样算法缺乏可靠性。本文提出了一个分类器以及具有相同计算步骤的确定性选择性采样算法，均通过将主曲线用作模型定义中的构件。
Considering the concept of time-dilation, there exist some major issues with recurrent neural Architectures. Any variation in time spans between input data points causes performance attenuation in recurrent neural network architectures. Principal curve-based classifiers have the ability of handling any kind of variation in time spans. In other words, principal curve-based classifiers preserve the relativity of time while neural network architecture violates this property of time. On the other hand, considering the labeling costs and problems in online monitoring devices, there should be an algorithm that finds the data points which knowing their labels will cause in better performance of the classifier. Current selective sampling algorithms have lack of reliability due to the randomness of the proposed algorithms. This paper proposes a classifier and also a deterministic selective sampling algorithm with the same computational steps, both by use of principal curve as their building block in model definition.
